Kumar, et al. [1]} propose that the order is determined by how easy the samples are to learn.
The authors propose a self-paced learning algorithm where training starts with only easy samples and the number of samples increases with each epoch until all the training data is used. They define easy as “a set of samples is easy if it admits a good fit in the model space”. The authors demonstrated on a SVM that their learning algorithm outperformed training on all of the training data.
